| name | medium-mcp-connector |
| description | MCP connector for Medium – enables AI-powered article creation, publication management, and content distribution through Claude |
| license | Proprietary/API |
| tags | ["writing","blogging","mcp","medium","publishing"] |
| difficulty | beginner |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
Medium MCP Connector
Overview
This skill enables Claude to interact with Medium through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Medium's REST API, allowing natural language control of Medium operations, intelligent automation, and AI-powered assistance for Medium workflows.
When to Use This Skill
- Article creation and formatting via natural language
- Publication management and submission
- Tag and topic optimization
- Reading statistics analysis
- Content repurposing and cross-posting
Architecture
┌─────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ Claude │────▶│ MCP Server │────▶│ Medium │
│ (Client) │◀────│ (TypeScript) │◀────│ (REST API )│
└─────────────┘ └─────────────────┘ └──────────────────┘
Core Concepts
MCP Server Setup
The connector implements an MCP server that exposes Medium operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.
Key Endpoints/Interfaces
/v1/users/:id/posts, /v1/users/:id/publications, /v1/me, /v1/publications/:id/posts
Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
const server = new McpServer({
name: "medium-mcp-connector",
version: "1.0.0",
});
server.tool(
"list_resources",
"List and query Medium resources with optional filters",
{
query: z.string().optional().describe("Search query or filter"),
limit: z.number().optional().describe("Max results to return"),
},
async ({ query, limit }) => {
const response = await fetch(`${BASE_URL}/v1/users/:id/posts`, {
headers: { "Authorization": `Bearer ${API_KEY}` },
});
const data = await response.json();
return {
: [{ : , : .(data, , ) }],
};
}
);
server.(
,
,
{
: z.().(),
: z.({}).().().(),
},
({ name, config }) => {
response = (, {
: ,
: {
: ,
: ,
},
: .({ name, ...config }),
});
data = response.();
{
: [{ : , : }],
};
}
);
server.(
,
,
{
: z.().(),
: z.().().(),
},
({ , timeframe }) => {
response = (, {
: { : },
});
data = response.();
{
: [{ : , : .(data, , ) }],
};
}
);
transport = ();
server.(transport);
Claude Desktop Configuration
{
"mcpServers": {
"medium-mcp-connector": {
"command": "node",
"args": ["path/to/medium-mcp-connector/index.js"],
"env": {
"MEDIUM_API_KEY": "your-api-key",
"MEDIUM_BASE_URL": "https://your-instance-url"
}
}
}
}
Best Practices
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Medium API rate limits
- Error Handling: Provide clear, actionable error messages for common failure scenarios
- Pagination: Handle paginated responses for large datasets efficiently
- Caching: Cache frequently accessed read-only data to reduce API calls
- Security: Validate all inputs before passing to the Medium API; sanitize outputs
- Logging: Log all API interactions for debugging and audit purposes
Example Prompts
"Create a well-formatted Medium article from this technical document with proper code blocks and images"
Security Considerations
- All API credentials must be stored as environment variables
- Implement input validation and sanitization for all tool parameters
- Use HTTPS for all API communications
- Follow the principle of least privilege for API token permissions
- Audit log all write operations for compliance tracking
Resources
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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